Summary of Bridging the Language Gap: Dynamic Learning Strategies For Improving Multilingual Performance in Llms, by Somnath Kumar et al.
Bridging the Language Gap: Dynamic Learning Strategies for Improving Multilingual Performance in LLMs
by Somnath Kumar, Vaibhav Balloli, Mercy Ranjit, Kabir Ahuja, Sunayana Sitaram, Kalika Bali, Tanuja Ganu, Akshay Nambi
First submitted to arxiv on: 28 May 2023
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the long-standing issue of large language models’ (LLMs’) limited capabilities in non-Latin scripts and low-resource languages. To address this challenge, we introduce a novel dynamic learning approach that optimizes prompt strategy, embedding model, and LLM per query at runtime. Our method achieves significant improvements over static baselines, operating efficiently in both offline and online settings, and generalizing seamlessly across new languages and datasets. By leveraging Retrieval-Augmented Generation (RAG) with state-of-the-art multilingual embeddings, we achieve superior task performance across diverse linguistic contexts. Our approach results in 10-15% improvements in multilingual performance over pre-trained models and 4x gains compared to fine-tuned, language-specific models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers better understand languages that use non-Latin scripts or are not commonly used. Currently, large language models struggle with these languages. The researchers came up with a new way to improve multilingual performance without needing to retrain the model for each specific language. They call this approach dynamic learning. It works by adapting settings dynamically at runtime to optimize results. This approach is efficient and can be applied to various languages and datasets. By combining it with another technique called Retrieval-Augmented Generation, they achieved better results than previous methods. This breakthrough could lead to significant improvements in tasks such as question-answering. |
Keywords
* Artificial intelligence * Embedding * Prompt * Question answering * Rag * Retrieval augmented generation
